{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"train.csv\");\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7a179b5860>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### Check if the gender plays a role in survival\n",
    "fig = plt.figure(figsize=(10,4))\n",
    "fig.add_subplot(121)\n",
    "df.Survived[df['Sex'] == 'male'].value_counts().plot(kind='pie')\n",
    "fig.add_subplot(122)\n",
    "df.Survived[df['Sex'] == 'female'].value_counts().plot(kind='pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Mr', 'Mrs', 'Miss', 'Master', 'Don', 'Rev', 'Dr', 'Mme', 'Ms',\n",
       "       'Major', 'Lady', 'Sir', 'Mlle', 'Col', 'Capt', 'the Countess',\n",
       "       'Jonkheer'], dtype=object)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### Split the titles from the passenger names which is itself a feature but also help in calculating missing median age values\n",
    "df['Name'] = df['Name'].map(lambda x: x.split(',')[1].split('.')[0].strip())\n",
    "titles = df['Name'].unique()\n",
    "titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Mr</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Miss</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Mr</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Name     Sex   Age  SibSp  Parch  \\\n",
       "0            1         0       3    Mr    male  22.0      1      0   \n",
       "1            2         1       1   Mrs  female  38.0      1      0   \n",
       "2            3         1       3  Miss  female  26.0      0      0   \n",
       "3            4         1       1   Mrs  female  35.0      1      0   \n",
       "4            5         0       3    Mr    male  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin Embarked  \n",
       "0         A/5 21171   7.2500   NaN        S  \n",
       "1          PC 17599  71.2833   C85        C  \n",
       "2  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3            113803  53.1000  C123        S  \n",
       "4            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].fillna(-1, inplace=True)\n",
    "\n",
    "medians = dict()\n",
    "for title in titles:\n",
    "    median = df.Age[(df[\"Age\"] != -1) & (df['Name'] == title)].median()\n",
    "    medians[title] = median\n",
    "for index, row in df.iterrows():\n",
    "    if row['Age'] == -1:\n",
    "        df.loc[index, 'Age'] = medians[row['Name']]\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 17 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,6))\n",
    "\n",
    "i=1\n",
    "for title in df['Name'].unique():\n",
    "    fig.add_subplot(3, 6, i)\n",
    "    plt.title('Title : {}'.format(title))\n",
    "    df.Survived[df['Name'] == title].value_counts().plot(kind='pie')\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Miss\n"
     ]
    }
   ],
   "source": [
    "print(df['Name'][2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AnandNoctis\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:25: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass Name     Sex   Age  SibSp  Parch  \\\n",
       "0            1         0       3    1    male  22.0      1      0   \n",
       "1            2         1       1    6  female  38.0      1      0   \n",
       "2            3         1       3    5  female  26.0      0      0   \n",
       "3            4         1       1    6  female  35.0      1      0   \n",
       "4            5         0       3    1    male  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin Embarked  \n",
       "0         A/5 21171   7.2500   NaN        S  \n",
       "1          PC 17599  71.2833   C85        C  \n",
       "2  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3            113803  53.1000  C123        S  \n",
       "4            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "replacement = {\n",
    "    'Don': 0,\n",
    "    'Rev': 0,\n",
    "    'Jonkheer': 0,\n",
    "    'Capt': 0,\n",
    "    'Mr': 1,\n",
    "    'Dr': 2,\n",
    "    'Col': 3,\n",
    "    'Major': 3,\n",
    "    'Master': 4,\n",
    "    'Miss': 5,\n",
    "    'Mrs': 6,\n",
    "    'Mme': 7,\n",
    "    'Ms': 7,\n",
    "    'Mlle': 7,\n",
    "    'Sir': 7,\n",
    "    'Lady': 7,\n",
    "    'the Countess': 7\n",
    "}\n",
    "#df['Name'] = df['Name'].replace(lambda x : replacement.get(x))\n",
    "\n",
    "for jef in range(0,len(df['Name'])):\n",
    "    df['Name'][jef]=replacement[df['Name'][jef]]\n",
    "    \n",
    "#for key in replacement:\n",
    "#    print (key, 'corresponds to', replacement[key])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.00</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>female</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.00</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass Name     Sex   Age  SibSp  Parch  \\\n",
       "886          887         0       2    0    male  27.0      0      0   \n",
       "887          888         1       1    5  female  19.0      0      0   \n",
       "888          889         0       3    5  female  21.0      1      2   \n",
       "889          890         1       1    1    male  26.0      0      0   \n",
       "890          891         0       3    1    male  32.0      0      0   \n",
       "\n",
       "         Ticket   Fare Cabin Embarked  \n",
       "886      211536  13.00   NaN        S  \n",
       "887      112053  30.00   B42        S  \n",
       "888  W./C. 6607  23.45   NaN        S  \n",
       "889      111369  30.00  C148        C  \n",
       "890      370376   7.75   NaN        Q  "
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AnandNoctis\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype object was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "df['Name'] = StandardScaler().fit_transform(df['Name'].values.reshape(-1, 1))\n",
    "df['Age'] = StandardScaler().fit_transform(df['Age'].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.797294</td>\n",
       "      <td>male</td>\n",
       "      <td>-0.557420</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.537975</td>\n",
       "      <td>female</td>\n",
       "      <td>0.649410</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1.070922</td>\n",
       "      <td>female</td>\n",
       "      <td>-0.255712</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.537975</td>\n",
       "      <td>female</td>\n",
       "      <td>0.423129</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.797294</td>\n",
       "      <td>male</td>\n",
       "      <td>0.423129</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass      Name     Sex       Age  SibSp  Parch  \\\n",
       "0            1         0       3 -0.797294    male -0.557420      1      0   \n",
       "1            2         1       1  1.537975  female  0.649410      1      0   \n",
       "2            3         1       3  1.070922  female -0.255712      0      0   \n",
       "3            4         1       1  1.537975  female  0.423129      1      0   \n",
       "4            5         0       3 -0.797294    male  0.423129      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin Embarked  \n",
       "0         A/5 21171   7.2500   NaN        S  \n",
       "1          PC 17599  71.2833   C85        C  \n",
       "2  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3            113803  53.1000  C123        S  \n",
       "4            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Fare'].fillna(-1, inplace=True)\n",
    "medians = dict()\n",
    "for pclass in df['Pclass'].unique():\n",
    "    median = df.Fare[(df[\"Fare\"] != -1) & (df['Pclass'] == pclass)].median()\n",
    "    medians[pclass] = median\n",
    "for index, row in df.iterrows():\n",
    "    if row['Fare'] == -1:\n",
    "        df.loc[index, 'Fare'] = medians[row['Pclass']]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,4))\n",
    "\n",
    "i=1\n",
    "for pclass in df['Pclass'].unique():\n",
    "    fig.add_subplot(1, 3, i)\n",
    "    plt.title('Class : {}'.format(pclass))\n",
    "    df.Survived[df['Pclass'] == pclass].value_counts().plot(kind='pie')\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AnandNoctis\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "df['Pclass'] = StandardScaler().fit_transform(df['Pclass'].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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I9KLH+eRvfyNvfwE+UquWnvcOItmXrlL6E5IVFKVxT5FcaOy60TI1ZMOIyvE7SHYe17z60QkkqzF+RRveyqJE5XhN4DfAO72z5NjfgA/XqqXnvININqX3ih1FslKpLnqMznPAwdq/TIYTleN9SBY8K+p2EZ1wBsl9nF1z7qiGbBGicnwk8D2gzztLAdwH7FOrlh71DiLZEpXjEvBrYDnvLAXwHHCQRqVlQVE5XhmYBOzhnaUAAsneZV+rVUtzvMNIdqT3P38H+CqaCtwKdwP71qqlB72DdIIasgWkc+vPAD7gnaVgpgDv1qpVAnrjaqN5JKvlHVOrlga9w4i/qBzvTjICrav1rXUr8P5atfSYdxDxly7ccSGwi3eWgplGMipd+L041ZANEZXjDYFLgA29sxTUTJI5+Bd6BxE/UTlekqTO9vTOUmD/BN5bq5Ze8g4ifqJyfCjwCzTTo12eB95Vq5Zu9Q4ifqJyvDnwByByjlJUgWTV5W8VeWGdHu8AWRGV4/cD/0bNWDstBvw2Ksdf8w4iPtKriFehZqzddgCuj8rxWt5BxEdUjo8BfoWasXZaEbg6Ksd7eQcRH1E53gO4HjVj7WQkC36dF5Xjwt7/qhEyICrHnwZ+6p2jy5xBsmeZ5uB3iagcrw5cgS56dNJkkiv4t3sHkc5IpwP/HDjMO0sXmUuyMtzZ3kGkc6Jy/AHgHKDfO0sXuZzkvrIZ3kFaresbsqgcf4pkJUDdx9J5V5EUVlfuOdFNonL8ZpJmbA3vLF3oFZKm7HrvINJeUTlegmSbln28s3SpY2rV0re9Q0j7ReX4EyQXPjTTrPOuJ1kobqp3kFbq6oYsLahfoGbM073ALlquu7iicrw1cBmwvHeWLvYqyaI6V3kHkfaIyvGyJFePt/XO0uV+AXy6yPe6dLv0tosTvHN0uTuAPYt07ti1nX1Ujj+OmrEs2Ai4LF3dUgomKsc7A39HzZi3JYE/R+X4Xd5BpPWicrw0ycbFasb8fQr4sXcIaY/03kw1Y/42B/4ZleMVvIO0SleOkEXl+GMkNzurGcuOvwEl3VNWHFE53gy4FpjgnUX+ayawa61a+pd3EGmNqBwvTjIytqN3Fvkf361VS2XvENI6UTk+AviJdw75H/8BdqpVS9O8g4xV142QReX4EOB01Ixlze7AWVE51v9LAaQLeFyGmrGsWQy4NCrHb/IOImMXleM+4CLUjGXRV6NyrIasIKJyvD9wqncOeYO3krynjfcOMlZd1ZBF5fjDJKv7ddW/O0cOBL7vHULGJirHAyRX7FfzziILNZFkmrCmkebfL4GSdwhZpBOjcnywd4huZ2Z7mdkDZvawmTXdJEfleE/g1+jcMat2As5PV5jNra55cUXl+G2oGcuDo6Jy/EXvEDI6UTnuB34HbOKdRYa1HvDHqBwv5h1ERicqx98CPuadQ0b0q3SvKnFgZr0k2xrtTXLP+gFmtlGjXx+V462AS4DC7n9VEO8lWfUyt7qiOUlv+rsAFVRe/CAqxwd4h5BROQPYxTuENORtwDmaJpw/UTn+EHC0dw5pyDjg4qgcr+8dpEttAzwcQng0hDAb+C3w7ka+MD13/D3JokiSfYdF5fhw7xCjVfiGLCrHPcC5wOreWaRhBpwdlWOd2OdIVI6/AxzknUOash/wPe8Q0rioHG9AMlVR8mNp4KJ0ARbprNWAJ4f8+SkamE6fTn+7AJ075s2PonK8nXeI0Sh8QwYcQ7JghORLP3Cu7nPJh6gc7wV8wzuHjMqX0hvWJePSE/qLAG0Tkj+boUUhPCxsBkAjy4tXgZ1bnEXar59kRHol7yDNKnRDls7b1rSO/FqZZK84ybC0aT7LO4eMyc/TlTEl234KbOodQkbt4+niYtI5TwFrDPnz6sAzw31BVI7fD3ypnaGkrVYDLkhXoc2NwjZk6cnFuRT439gl9tUqVZn3K5LmWfJrGWCS7ifLrqgcfwT4qHcOGbOfR+V4Q+8QXeRm4E1mtraZ9QP7A5cu6pOjcrwucGanwknb7Ah82ztEMwrZrETleBxwIaDpbsXw46gcr+kdQt4oKseH0eAN0pJ5uwBf8A4hbxSV442Bn3nnkJZYkuR+siW8g3SDEMJc4DPAX4H7gAtDCPcs7HPTNQd+jaYEF8VXonL8du8QjSpkQ0Yy93d77xDSMhNIFvnQ1fsMSTcXPtk7h7TUiVE51pYFGZJOuzkP0Al8cWyMFtPpmBDCZSGE9UMI64YQjh/mU79MsvqsFEMP8OuoHOdilczCNWRROd4GXeUtop3R/2tmpCeJ56LlgItmPPCbdD85yYbPkywIIcVyeFSOt/YOIYl0Gulx3jmk5dYBTvQO0YhCNWTpMqU/p2D/LvmvE6Jy3PCGjtJWRwM6mSimzcnZ3PuiSu+FrnjnkLboIbmfTOcrztL/g1+RXJCS4jkiD1MXi/aD4NPAW71DSNssRnL1Xht8O0pvev6qdw5pq6N08SMTTkH3sxTZliTnLeLrk2iqYpH1AKdnfdXFwjRkUTleGV3V7QZvAXK7E3tB/BBdSSy6XnSPi6uoHL8LeJ93Dmm776TnL+IgKscDwLe8c0jbbQh8yjvEcArTkAEnAAPeIaQjjk5/iEqHReV4d7SqYrcoReV4F+8Q3SjdAPon3jmkIwaAk7xDdLFvohW5u8WxUTlexjvEohSiIYvK8WbAR7xzSMcsD5S9Q3SbdJ69Thy6y/e1uqmLrwFre4eQjjlAFz86LyrH6wCf884hHbM8SQOeSYVoyIAfUJx/izTm8+kN79I5BwNaEr27vBU4yDtEN4nK8UTgSO8c0nHf8Q7Qhb4HaEXZ7vLZ9D74zMl9ExOV472A3b1zSMctjlYf65ioHI9HSwJ3q+9E5Xgx7xBd5ItoIY9utH1UjnfzDtEtonL8NmBf7xzScf0kexVnTu4bMnKyv4C0xcFROY68Q3SJI4A1vUOIizXRHoAdEZXjZYHPeOcQN8d4B+giR3sHEDf7pvvOZUquG7KoHO8IbOGdQ9yMA77uHaLo0m0GvuydQ1yVo3K8tHeILvB5YIJ3CHHzzvS8RtooKsebA3t55xA3Rga37sl1Q4ZuxhQ4JCrHGrlprw8AWpa5uw0Ah3qHKLKoHE8gaciku2mUrP20KJh8KCrHa3iHGCq3DVl6Eq7lt2Uc+uHabrrwIQCfi8pxr3eIAvsckNklmaVjdonK8du9QxRVurLi+71ziLtxwFHeIYbKbUNGsru9Tg4EknvJdBN8G0TleFtgG+8ckglrA+/1DlFE6aIpuk9P5tNU/Pb5Mjp3lMTH01VtMyGXDVn65vVx7xySGUsC+3mHKCiNjslQej20x/uAzJwYiLu9tK1L66WbAmvPWplvSeAw7xDz5bIhAw5Eb17yvw7xDlA0UTleBU3tkP/1zqgcb+QdooA+5h1AMqUHNQ7tcADJljki833UO8B8eW3IPusdQDJnh6gcr+0domAOJ5lnLTLUp7wDFElUjtcCdvHOIZlziHeAAtKFD1nQ+lE5fod3CMhhQxaV43cCm3vnkMwx4GDvEEWRLnX/Ce8ckkkfjsqxrjK3ziEkP79EhlovKsc7eIcoiqgcbwJs5Z1DMikTo2S5a8hINqgVWZiDo3KsE5vW2AlYyTuEZNIywD7eIYog/Xl1iHcOySyN6LSOvpeyKB+IyvGS3iFy1ZBF5bgfeJd3DsmsdQBdUWyN93gHkEzT66M1dgYi7xCSWftpBeGxS2d8HOSdQzJrKTJwv3yuGjLgncDS3iEk0w7xDpB36VV7nXDLcN4VleM+7xAFoIUbZDhLkoETxQLYGVjBO4Rk2ge9A+StISt5B5DM2y8LQ885tw2wqncIybRlgB29Q+RZVI570IwPGZmmB4+dLjDKSHaJyrHrgI8aMimapYC9vUPknN68pBHv9g6Qc1sBy3uHkMzbVaPRo5fO+Ph/3jkk8/pxPnfMTUMWleN1gfW9c0gu6Mr92LzXO4DkghqysdnTO4DkwgCwnXeIHNsCWM07hOSC66BPbhoyNDomjdPCHqMUleM3Axt455BcWDMqx2/xDpFje3kHkNxQ8z56mhYsjdrLc6XuPDVkKipp1KZROV7WO0ROabqiNEOjZKMQleNlgG29c0huqHkfPX3vpFErAlt6HTwXDVm6SMNO3jkkN4xkRU5pXiZ2rJfc2N07QE7tBvR6h5Dc2DIqx1olsElROR5PskiVSKPczh1z0ZABuwLjvUNIrmja4uhs7R1AcmWLqByrsWiepqBJMwxd/BiNLUkWaxBp1PZeB85LQ7aLdwDJHTVkTYrK8ZokQ/YijVoC2Mg7RA5pJFqa9XbvADmkxVCkWdlsyMxsmpm9sqhHp0ICm3TwWFIMb4nK8VLeIRqRoTrT6JiMxlbeARqVhVqLyvHiwJs6cSwplM29AzQqC3WWcju5ltxaPSrHLqtyDru3RQhhaQAz+xbwLHAOydD5gUAnN1DTFVhpVh/wNuAK7yAjyVCdqSGT0dgKOMs7RCMyUmuboPvHpHmbReXYatVS8A4ykozUGWiETEZne+DiTh+00SmLe4YQfhZCmBZCeCWE8HNg33YGmy9djWqVThxLCidv+5G51VlKDZmMRm5GyIbwrLXcjHRIpiwNrO0dokme546rAat34lhSOC4jq402ZINmdqCZ9ZpZj5kdCAy2M9gQG3foOFI8eTvxcauzdO8Nt+VeJdc2i8rxsLMtMsjzPS1vP5ckO/L22vGsM93qIqPl0nc02pB9CPgA8Fz6eH/6sU7QdEUZrbW8AzTJs87WBwY6dCwplsXI38mPZ63l7aRasiNvrx3v9zSR0XB57TR0VTOEUMNvA1A1ZDJaa3oHaIZzneXthFqyZQvgdu8QjXKutc2cjiv5l6uGzLnOtHCOjNaaUTnur1VLszt50IZGyMxsfTO7yszuTv+8mZl9s73R/ktTFmW0JqT3IOaCc51FHTqOFNMa3gGa4VVr6dYSGomW0drUO0AznN/T1JDJaPUC63T6oI1OWTwd+BowByCEcCewf7tCLUAjZDIWeZq26Flnefo+SfbkbeElr1rTIgMyFi7LcY+B53uapizKWHT89dNoQ7ZECOHfC3xsbqvDLCgqxwPk7weQZEuepi261FlKDZmMxareAZrkVWsrdeAYUlyLpedFeeF17jgOvafJ2HR8hLXRhuwFM1sXCABmth8wuW2pXrduB44hxZanH8pedQY5m3ImmZO3hsyr1tSQyVit7B2gCV51tjLa60/GpuODQY0uVXwEcBrwZjN7GniMZIO/dluuA8eQYsvTCJlXnYFOFGVs8jZl0avW8nQyLdm0EvCAd4gGedXZxA4cQ4qt46+hRhuyx0MIu5nZkkBPCGFaO0MNsWyHjiPFlacRMpc6S/cgW6ETx5LCWjkqx1arloJ3kAZ5vafpwoeMVZ6aeq8608V8GauOv4YanbL4mJmdBmwHTG9jngXlZoU8yaw8NWRedbYsMK6Dx5Pi6QNW9A7RBK9aU0MmY5Wn15BXnWmETMaq46+hRhuyDYArSYafHzOzn5jZO9oX6780QiZjlaeRH686W74Dx5Diy9O0Ra9ay9PohmRTnl5DXnWmhkzGKpsjZCGEGSGEC0MI7wPeAkwArmlrssSEDhxDii03Iz+OdZab75FkWm5Wf3OsNU2lkrHKTbOhOpMcy2ZDBmBmO5rZz4D/AIsBH2hbqtct1oFjSLHlqtlwqjPrwDGk+Bq9JzkTnGqtvwPHkGLTe9rIlu7AMaTYOv4aaugN1MweA24HLgS+HEJ4ta2pXqc3Lxmr3Lx5OdaZGjJphdw0ZI61lpvvkWRWbl5DjnWmJe9lrDr+Gmq0sDcPIbzS1iQLN97hmFIsuWnI8KuzhkfKRYaRmxNF/GotT98jyaY8vYZUZ5JXHX8NDXtAM/tKCOF7wPFm9obljEMIn2tbsoRGyIYxgen1v43/ykN9DOqHzyIM0jMDnvCOMawM1JlGyBZhSWZM+1jv5Xfu3/ePcYsxWz+PhvFEWHEQSt4xhuVdazeOP+L+ccx9rp3HkGKbGpZ6QXU2coQ2P3/u/XjcqVe/recerWS+aAGe6ugBRzqRvy/99ZZ2B1kEDTsP48z+H9yxkk3dwTtHxr3sHaAB3nWmN68hehmc+95kfmk5AAAgAElEQVTe6247vPfSuevY5C3MeLt3pjyYaNPyMNLqWmsr28vrA6t5HFuKYaJNu9s7QwO839MGnY6bG0+H5Xsm2rQtvHNkWMdfQ8M2ZCGEP6W/vTOEcFsH8iyo7nDMXNjW7r13S3uwE8vH5t0c7wAjyUCdqSED3t5z9z1f6LvkxS3twY17LGztnSeHMn8SlIFam+twTCmWzL+GMlBnmf9Z5O2cubu/6VN9f/aOkWXZasiGOMnMVgEuAn4bQrinjZmGeqFDx8mVHuYNntn/fTPTvT8NyHxDNoRXnXVtQ7auPf34F/suru3ec0vUb4Mbe+fJucyfKA7hVWt5+h5JNuXpNeRVZ7M6dJzcepoVVnktjH9gCZu1gXeWjJrd6QM2ug/ZzsBOwBTgNDO7y8y+2c5gqRc7cIzc+Wbfb65b0mZt6J0jJzpeVKPlWGczO3CMzFiO+ovf7Dvnn3eNP/Seq8Z/ea1S70079tvgWt65CmCqd4BGOdZap1aZk+LKzWvIsc7ycKuCu3/O22yyd4YM6/hrqOERlhDCsyGEU4FPkSxjekzbUr1ODdkCVual5z7a+5e3eOfIkVxdKXOqs2c7cAxXizFrxqG9l91ww/jP3Hzr+MMHPt53+Q5L2wyNiLXWFO8AzXCqNS3oIWOVq9eQU5291IFj5N7Zg3us6J0hwzrefzS6D9mGwAeB/UhC/hY4qo255lNDtoDz+o9/xIy3eefIkae9AzTKsc5eIhlJLNQqgsa8eXv13HzHZ/t+/9qG9sRmZmzvnangctOQOdZark6mJZNy8xrSuWO23Thvow0Hgz3fa0GN2RtlsyEDzgLOB/YIITzTxjwLUlENUeq58dZ1eiarGWtOzTtAE1zqrFYthagcPw+s3qljttNb7KEHj+y7ePLbe+7eoNeCRpM7YzqV+gzvEE3wek/Lzcm0ZFaeZjTo3DHTzO4Laz24idXUkL1Rx0dZR2zIzKwXeCSEcEoH8ixIRZXqZ86sk8f9bKJ3jhyqeQdohHOdAUwmxw3Z6jblmc/3XvLQ//XesOpiNmd9YH3vTF0mT6NjnrWWp5NpyaZcNPXOdaYpiw06d3DXcSf2nOEdI4uyN0IWQhg0s4lm1h9C6PQCCWrIUieP+9mN/TZ3R+8cOVTzDtAI5zqDHJ4oLs2r9cP64rsO6r1y6WWZvpkZq3pn6mK5acicay0XJ9OSabn4We1cZ7n5eeTtj4Nv3+SEvjNmmTHeO0vGdHyV90anLD4OXG9mlzJkhZ8QwkltSZWqVUvTonI8BxjXzuNk3Zvsqdq7em7azjtHTtW8AzTBpc5SuVhtaRxzZ+/Xe83tn+r907w17fm3mKG9+LIhbydAXrWmhkzGIgDPe4dogte540tROZ4KLNPO4xTBayy25PMsc8tKTN3KO0vGPNrpAzbakD2TPnqApdsXZ6GmQHdf+T6v//gXzYi8c+RUzTtAEzzrLMNXXUPYseeOu7/Q97upm9vDm/QY23gnkjd4yDtAk7xqLcN1JjnwEpV6nvbW9HxPewjYusPHzKVLB9/22mF9l3nHyJqOv6c11JCFEI5rd5Bh3EsXN2Qf6738XytYXQt5jM5skjeDXHCus8ytRvlme+LRI/suenKXntvXGWeDm3rnkWHd6x2gGY619igwjya2nBEZ4mHvAM1wfk9TQ9agXw/usa4asjd4sNMHbHTZ+3+QDJX/jxDCLi1P9Eb/AXbrwHEyZylee+Ubfb9Z1ztHjj1JpT7PO0SjnOvs7g4cY0Qr8vKUI/r+cN++vdeusJTN3BBYxzuTNCRXDZlbrVXq06kMPAK8qa3HkaK63TtAM5zf0/I2au/mybDiajNC/4OL22wthpWoU6l3fGpwo1MWvzTk94sB+wJzWx9nof7ToeNkzhn9P7it14IW8hi9mneAJnnW2e3AINDboeP91xLMfPXg3ivu+GjfX8avyNQtzNih0xlkzHLVkOFba3eghkxG5w7vAE3yrLOOj3Dk2XXzNp28e++tasgSLs18o1MWb13gQ9eb2TVtyLMwXdmQbWUP3LeN3a8FC8am5h2gGZ51VquWXovK8QPARp04Xg/zBvfpueH2z/T9Ydab7OnNtdl5rj1Lpf6yd4hmOL+n3UGyUa5Is3LVkDnXmRqyJpw9uOfE3XsX/O/qWi6vnUanLC435I89wFbAym1J9EYPA68AEzp0PHfGvHln9383mHV+tKJg7vEO0AznOgO4lTY3ZNvYffcd2XfJlG167tuw18KW7TyWdEzeRse8ay1XJ9WSGQG40ztEM5zr7C6S+8j7O3S8XPvXvI02mhdsSo+FFbyzZMAtHgdtdMrirbw+D3guycjDoe0ItKBatRSicnw7dM80pq/1nXfdUjaza/69bfQP7wBNcquz1H+AD7f6SSOb/OQX+i55dO+ef68x3uZuCGzY6mOIq0zcf9gkz1pTQyaj8SiV+nTvEE3yPHecFZXj24BtO3G8vAv09Nwf1nhgI3tCDRnc4HHQYRsyM9saeDKEsHb654+QzAGu0dmrov+hSxqyFXl5ymG9l23unaMAXiInJz4ZqrOWzVdYhmkvf7Lvz3d/qPfvy0zg1U3MWKNVzy2Zc513gEZlotYq9SeoDLwMLNuR40lR5OL9DDJSZ4kbUEPWsPMHd+37ds9Z3jG8zQZu8zjwSEvv/pIkHGa2A3AiMAmoA6e1N9r/6Jr7yH7Tf8KDZgx45yiAq6nU37C6U0Zlpc5uYyErYjVqPLNnHtz71xuuG/+5f982/pNLHt73p3cO2KubmmEtzCjZc7V3gCZkqdZEmpGn86Cs1NmNHTxW7v1u8B0bh5D8v3Wx/1Cpz/I48EhTFntDCC+lv/8gcFoI4RLgEjPr5PKrefpBNGp79vz7tvV7nn67d46CyNN0xUzUWa1amh6V4weBDRr/qhB277n1js/3/W76xlbb1Izt2xZQsuheKvUp3iGakIlaA64EOrH0txTHld4BmpCVOnOZepZXr7L40i8w8J8VqL/VO4sjt9fMSCNkvWY2v2nbFfj7kL9r9P6zVrgfyNvc6aaMY+7sU8f9ZBnvHAWSq4YsI3UG8M9GPmlje+zhM8d975qHx3948un9J22xSU/tHRrZ7UpXewdoUlZq7S8dPJbk30vAzd4hmpCJOqtVS08AT3fqeEXwp8Htp3lncJbZhux84Boz+yMwA7gWwMzWIxl67ohatTQIXN6p43n4wbhf/Gu8zV3bO0dBPE+lnqcVFjNRZ6lF1tmqvDD5xL7Tr7l3/CEPxuO/sd4uvbfv2GfzVu1kOMmcq70DNCkrtXY78FwHjyf59jcq9XneIZqQlTqDfI0sups0uMe63hkcDeL4ehn2SkUI4XgzuwpYBbgihDD//pIe4LPtDreAS4D3d/iYHbG2PfPE/+v513beOQrkau8AzchYnV0JzAHGASzJjGmH9l5+x8F9Vyw5kVc2N2OVDueRbOvUnkItkZlaq9QDlYEraMOqplJIuRpRzUydJS4HPtLhY+bW42Hl1WeGcQ8vZnPW887i4HrPPTVHHDoOIbzhpsgQgsemaTEwCxjvcOy2Or//+OfNWNM7R4H8feRPyZas1FmtWpq2XvmPV7+n9/plDu+9dO46NnkLM7RBuSzMHVTqz3uHaFZWao3kJFsNmTTir94BmpWhOruCZORD+7o26Pp5mzy1a+9t3diQ/dnz4CNNWcyMWrU0naSwCuXg3r/esLK9vJV3joLJ0/1jmXP/+EPiH4z75dbr9kze3ozFvfNIZl3oHSDnrgDyNA1NfNxJpT7ZO0Re1aqll8nR1hxZMGlwj4neGZz8yfPguWnIUpd4B2ilJZkx7di+cyLvHAVTo1L3uApXGH0274/eGSQXLvAOkGuV+gu0cO8/KazcjY5l0B+8A+TJtfM23XhesBe9c3TYw1Tq93sGyFtDdinJ/S2FcPq4H/6n1+bpnpzWOtM7QO5V6jW0T5IM7z9U6o94hygAjTLKSHThY+x+zxj22Ow2gZ6eB8Pqrs2JA9fRMchZQ5YOPRdiOtpb7KEHtu+5V/fmtNYg0PXbzLeIThRlODpJbI1zgLneISSz7qRS1yjqGNWqpcdpcEsXSZw/uHOu+oMW+LV3gDx+w3/nHWDsQpjUX51jpptMW+wvVOpPeYcoiEnoRFEWTQ17K1TqzwGXeceQzNIFxtbR7JkmXDK4w8YhFGdG2ghup1Lv5IblC5XHhuwP5PxG6C/3XXDdBJuxiXeOAjrdO0BhJDeRu644JJl1UzqtVVpDJ4qyMHOA33iHKJCLgW7f9Lhh01liwotMuMs7R4dk4mdw7hqyWrX0HPA37xyjNZH6C4f3Xrqpd44CmkyyNYK0zmneASSTfukdoGBitEm0vNGf0oVfpAVq1dJraKp1U+LB7bqhgZ0FnOsdAnLYkKVO8Q4wWuf2n3B/j7GMd44COotKXVPsWuuvwOPeISRTpgDneYcolOTnlkZCZEGZuGpfMPqeNuHswT3W9s7QAZdSqb/kHQLy25D9BcjdCjC79tx6+5t7ntRCHq0XgDO8QxROpT4PfV/lf/2CSn2Wd4gC0omiDPUMyXmOtFCtWroB6JZpeGP2WFh1zVlhXNFX083MTKBcNmS1aimQs1GyPubO+em4U5f2zlFQV1GpP+odoqDOQIt7SGIO8HPvEIVUqd8LXOUdQzLjZ1Tqg94hCur73gHy5IZ5GxZ5obRbqdSv9A4xXy4bstSvgUwMMzbiu+NO/9diNmdd7xwF9SvvAIVVqT9DRuZXi7sL08VepD2+7R1AMmEq8GPvEAV2PpqK37BJg3sW+Rab73oHGCq3DVl6g+ap3jkasZY9+9T7eq7dxjtHQT1BsumjtM93SPZ4k+6Wq1kJuVOpX4P2ShI4hUr9Fe8QRVWrluYCP/TOkRfXzNt8k3mBl71ztMFDwCXeIYbKbUOWOgXI/A+u8/u/84wZi3vnKKhvUanP9g5RaJX6w2jRgW53JZX6zd4husC3vAOIq1eAH3mH6AK/IlmgSEYwj57eh8Nq93rnaIPvp/fJZ0auG7JatZT5of0P9V5506r2kkbH2uNB4GzvEF1Co2TdKwBl7xBdoVK/CrjeO4a4+TGV+lTvEEVXq5ZmoBH/hl0wuHOue4WFmExy21OmFOGbfDIw3TvEwizBzFe/1Xf2Gt45CuwY3fjcIckome4l604XUqnf6h2ii+hesu40neR8RjrjVLT/X0MuGtxhoxAKtbhXJYurBee+IatVSy+S0R9ivxh38i19Nm9V7xwFdTtwoXeILvNtNErWbeYA3/QO0VUq9b8C//aOIR33Uyr1F71DdItatTQNONo7Rx68wlIDL7F0UbYLuIuMbueT+4YsdQKQqb0SNrNHHnpnz11v985RYEdRqQfvEF0lGSX7mXcM6ajT0/936awjSaaKSnd4FjjRO0QXOgO40ztEHlw+uE3m12to0FFZnVlViIasVi3NBA73zvG6EM7pP3GGGX3eSQrq91Tqf/cO0aW+SXLyIMX3Klpkwkel/i8yehVX2uIoKvW6d4huU6uW5gFHeefIg0mDe67lnaEFLqdS/5t3iEUpREMGUKuW/kayv4S7I/suvm7AXtvMO0dBzUQ/QP0kyzHr+98dvk2lrnss/HwVrQTXDa6kUj/PO0S3qlVLVwKxd46seyisHs0OfY955xiDuWT83KUwDVnqSPDdL2FZXnnps71/2NgzQ8H9kEo9zz8U8i85edAIZbHdAvzAO0RXq9RfAr7iHUPaahZwhHcI4UiSi70yjJvmbfiEd4Yx+DGV+n3eIYZTqIasVi09h/PyzOf0V+/psbCcZ4YCewzNs8+KIwDt/1ZMc4BDszrPvstMAq71DiFt8z0q9Qe9Q3S7WrX0EHCsd46smzS4+zLeGUbpEXKwOFWhGrLU6Tjt47JTz+13bmy1d3gcuwvMAT5Ipf6qdxABKvX70QhKUZ1Ipa4b3bMgWbjocJKff1Isj5AsSCbZ8EOSmQGyCP+Y95aNQyBv9zoG4DAq9de8g4ykcA1ZrVoKwCfp8BtYL4NzfzHu5MXNsE4et4t8nUr9Zu8Q8j+OA7Q/VbHcAxzvHUKGqNTvQTMDimYe8AkqdU2Ty4hatTQIfARNXVykQXr7Hgmr3OOdo0k/pVL/x6L+0szONLPnzezuToZamMI1ZAC1auke4PudPObxfWdct5jNeVMnj9lFLiO5eiVZUqnPBvYHpnlHkZYYBD6W/r9KtnwLuNo7hLTMd7RScPbUqqV7gW9458iyCwd3ztN2HA8w8n24ZwN7tT/KyArZkKWOpUNvYGvY809/sPfqrTtxrC70DHCI9hzLqGSPqgxtOSFjcDSVujYkzqLkfr4DAK16mX9/J5ldINn0I+Cv3iGy6oLBnTYOgbneORowCziQSn3GcJ8UQvgn8FJnIg2vsA1ZrVqaC7wfqLX7WOf3f+dpM5Zs93G60DzgICp1Lf2cZZX6uSSLD0h+xUDVO4QMo1J/FvgQyc9Fyafk/7BS1/9hRqV7k32IDpw75lGdpZaZylJ5mLZ4BJV6rm6pKGxDBlCrll4A3k2ywWlbvL/36n+vbi9s067n73LfGW7ur2TKEYBWC8unx4GDNQqdA8k0N23WnU+DJM2YRjkzrlYtvQS8Dxh2dKVb/WVw66neGUZwOpX6Gd4hmlXohgygVi3dSXKjZstPNhZn1msn9v1q1VY/rwDwT3TikR/J6pfvg9ytwNTtXgPene55JfnwbeBK7xDStON0gTE/atXSbWg6/kJNGtxzTe8Mw7gJ+Ix3iNEofEMGUKuWLgG+0+rn/em4U27us3mrt/p5hSkkVxK1D1KeJKvB7YuW6M6Tj1Kp3+EdQpqQTHc7EE2pypM/odVLc6dWLU0CfuadI2vuD2uuPTv0Pu6dYyGmAPvldWGqrmjIUscCf2jVk21sjz28c8/tb2vV88l/vQzsSaX+tHcQGYVK/Srg494xpCFHU6lf6B1CRqFSf55kZbAXvaPIiG4C9td9Y7n1eZKVnmWIW+ZtUPPOsIDpwD5U6k8180Vmdj5wA7CBmT1lZoe2JV0DuqYhS/cn+zDQkr0GftN/4qtmjGvFc8l/vQLsRaV+m3cQGYNK/dckF0Aku35Ipd7yWQPSQZX6A8D/oftcsuwhkpPEzG9KKwuXLhC3H3C9d5YsmTS4xwTvDEPMBt43mlWCQwgHhBBWCSGMCyGsHkJwu/esaxoygFq1NJ1kkY8nx/I8n+n9/XXL2vTNW5NKUq8CJS27XRCV+reAM71jyEL9ikr9S94hpAUq9RtIVhPWNOHseZpktscL3kFkbGrV0gxgH+Au7yxZcdW8t24SQibuGZ+/GvffvIOMVVc1ZAC1aulR4J3AI6P5+mWY9vIX+y5+c2tTdb2ZwP+jUr/OO4i01CeBi71DyP+4kOT/RYqiUo+Bg0hW8ZNseB7YlUr9Me8g0hq1amkqsCeg/1NgLn3jHgsr3+udA/g0lfpF3iFaoesaMoBatfQ4SVPW9F4Kk/q/e3ePheVbn6przQbemy7nLEVSqc8F9gfO8Y4iAFxOciVR97IUTXIv4Mdpw2rC0rSXgT3SKaVSILVqaTKwG1pQB4ALB3fyfi/5EpX6L50ztExXNmTw38LaEWh447h39Nx112b26Dval6rrzAU+QKX+F+8g0ibJSpkfAU7zjtLl/gjsS6WuqW1FVamfDRxAcpFLfDwFvFMrlxZXOsvqHUAWRodcXTC404YhuIzMzwMOo1L/ocOx26ZrGzKAWrX0IrALMOJUuR7mDZ427of9Zlj7k3WFQeBAKvU/egeRNqvUA5X6J4EfeUfpUr8gaca0+EPRVeoXkKy+mIV7O7rN3cD26fYfUmC1aulpYAegq+95f5kJy9VZstOv99nAB6nUf9Xh47ZdVzdkALVq6RWSecFXDPd53+o767olbPYGnUlVeFOB92jJ7S5TqR9JG/YDlGF9k0r9cO3p10WSzYffSbKohHTG1cA7ml1yW/IrvaC/K3CVdxZPVwxu9XIHD/cqyaqlhbw3vesbMoBatfQayfLBv1/Y36/GlMkH9l61ZWdTFdZdwFZU6n/2DiIOKvWjSaYwzvSOUnBzgY9RqWsz2m5Uqd8FbM8o7pOWpl1Isl2LRiW7TLpydwk4zzuLl7MH91y9Q4d6BtilCKspLoqFoHuA54vKcS9wPPAVeH1q4jX9X7hxrZ7nt3MLVhznkcz71Z4s3a4ysCXJBZA1vKMU0MvAh3RvplAZWAb4A8n90tJ6JwNHUanrRKrLReX4y0CVLhzoeGj8h58YZ4NrtvEQ1wLvp1J/ro3HcKeGbCGicvx/wCRg2ff2XHvzyf0/39o7U87NJVkN5xTvIJIhlYEVgIvQyWIr3QDsT6X+hHcQyYjKQB9wHFCmC08W22Qq8ImiLLctrRGV4z1ILjxP9M7SSb/t//Y12/Xc16738VNJLnrMbdPzZ4YaskWIyvFaSzDznDvGHxaNs0FdxR+9Z0lWUrzWO4hkUHKy+EPgc95Rci4APwC+3g1vXDIKlYGdSbagWM07Ss5dT7Ig1ePeQSR7onK8BsmFxm29s3TKu3pu+s/P+k95a4uf9jWSix7ntvh5M0sN2XAqA+OACvBVoNc3TC79i2SY+RnvIJJxlYF3A78EVvKOkkMvAB+hUr/MO4hkXGVgInAG8G7vKDk0SHJLw7e0SI4MJyrH44CvA98AxjnHabs+5s55aPzBM81YukVPeSPwUSr1+1v0fLmghqwRlYHtSa4srusdJSfmkFytP1b7HknDKgPLAz8H9vOOkiOXk9yXqRX1pHGVgcOBk4DFvKPkxJMko2Ka6SENi8rx5sDZwBbOUdru6v4jb4h6ntt+jE8zEzgGOKkbL3qoIWtUZWBJkqlVnwDtRTaMa4DDqdTv8w4iOVUZeB/wE2AV7ygZ9hTweSr133kHkZyqDGxAsjfgXt5RMmwuyUWiY6jUp3qHkfxJR8u+BnyTAo+Wfab399d/adxFbx/DU3TlqNhQasiaVRl4K3ACyd5l8rrnSRbuOMc7iBRAZWCApM4+AfQ5p8mSuSQn0cdRqU/3DiMFUBnYh2S07E3eUTLmb8AXqNTv9Q4i+ReV401J6mw37yztMJH6C7eMP3w5s6YXDnqB5NagX3TjqNhQashGqzKwE3Ai0O3L4c8kOUGsah8WabnKwPok921oGiP8EziCSv1u7yBSMJWBfpKFdY4GJjin8fYIyapuf/QOIsUTleO9ge8Bm3hnabU7x3/87gn2WqP/rvnnjidSqb/Sxli5oYZsrJLFCI4HNvaO0mHzSO6rO5pK/UnvMFJwlYGtSPZ42dU7ioNrSRYSuNI7iBRcZWAlkpHpg+m+kempJD9jTqZSn+0dRoor3fP2o8C3KNDU/B+O+/nV+/Zeu9MInxaAc4FvaHuW/6WGrBUqAz3AQSR7vUS+YdpuFnAx8AMq9du9w0iXqQzsTlJnY715OA/+QdKIXe0dRLpMZWBNkhGzwyj+iFmN5Er9GZoGLJ0UleMlSBqzIynAonGb2SMPXTr+6EVNfZ5N0oidpFkeC6eGrJWSaR+HAYdTvBGzB0mWJZ9Epf5iI19gZmcC+wDPhxAKNzwvjpIRsyOA/SnWSnHzSFZOPJFK/XrvMNLlKgMTSN7TPg8UbT/Of5OsBvy7br93RXxF5bgHeA9wFPA25zhj8vD4g57qs3mrD/nQSyQL4/yESv1Zp1i5oIasXSoDWwAHAgeQ3404ZwO/B35Jpf6PZr/YzHYApgO/VkMmbZHsq3QoyUWQyDfMmDxEsjzyJC1hL5mTbOD+fuCzJPdN53Wl4VlATDIt8TrvMCILisrxdsCngfcBSzrHadqF/cdds03PAzsCtwBnkrynveYcKxfUkLVbMp1xZ5Ipje8jH9M/HgVOA86iUn9+LE9kZhHwZzVk0lZJne1KUmP/D1jVN1BDpgMXkdSZ9jeSfKgMrAHsS9KgbU/2m7OZwF9Iptr/SQsISB5E5XgpkvezA0je2/KwZP4ju/fccvrp/Sf9SauTNk8NWSdVBhYjOVk8ENiD7Ey1mkqycMA/SfYRu4VKvSUvDDVk0nGVAQO2Ad5LMg1kA99A/+Mu4Argr8C1VOoznfOIjF5lYDWS5mw/4O3Q9JLX7TKDZOrvRcCfdW+Y5FlUjpcjOXfcHdgFWNk30X/NAW4CrgL+VKuWbnXOk2tqyLwkU0A2AbYCtkx/3Qzo78DRp5A0YNeQNGF3UqnPa8eB1JCJu8rAeiRX8rcBtgU2pzN1FkiW0P43SRN2BZX65A4cV6TzkvvN5tfYdumvK3To6I+QnBjemP56u1ZKlKKKyvHGJKNmu5DU2kodOvQs4G6SBaeuAq6tVUuvdujYhaeGLEuSRUE25fUmbUtgdWA5mluCeB7JRs3PLvCokTRi97VqBGwkasgkcyoD44EtgK2B9UgWK1gDWBNYkeamYAWgTrK5ZQ24D7ifZCTsDk2Pkq5WGViH5IRxU5L3stWG/LpEk882FXgqfTwNPAH8B7iRSv2FVkUWyZuoHC9PcoF//mNjkhpbkebvQxskeT97juTe5ruHPB6qVUtaAKdN1JDlQTIFawBYHpgILE4y3XF8+hgHvMjrjdeUrKwapYZMciVp1uZfBBm3wKOPZErWyyRvWC8CL1Kpz/UJK5JjlYHleP2kcX59jSO5yDEHmEty/9dk4CktDCDSvHRp/RXTx/yL+30kFx4HSersVZKL+M8DL9WqJTUGDtSQSduY2fnATiSN5HPAsSGEM1xDiYiIiIhkiBoyERERERERJ1lZEUlEpJDMbC8ze8DMHjazsnceERERyRY1ZCIibWJmvcBPgb2BjYADzGwj31Qi2aCLFSIiCTVkIiLtsw3wcAjh0RDCbOC3wLudM4m408UKEZHXqSETEWmf1YAnh/z5qfRjIt1OFytERFJqyERE2mdhe5ppJSURXawQEfkvNWQiIu3zFMmm0/OtDjzjlEUkS3SxQkQkpXskbO8AACAASURBVIZMRKR9bgbeZGZrm1k/sD9wqXMmkSzQxQoRkZQaMhGRNgkhzAU+A/wVuA+4MIRwj28qkUzQxQoRkZQ2hhYREZGOM7N3AT8CeoEzQwjHO0cSEXGhhkxERERERMSJpiyKiIiIiIg4UUMmIiIiIiLiRA2ZiIiIiIiIEzVkIiIiIiIiTtSQiYiIiIiIOFFDJiIiIiIi4kQNmYiIiIiIiBM1ZCIiIiIiIk7UkImIiIiIiDhRQyYiIiIiIuJEDZmIiIiIiIgTNWQiIiIiIiJO1JCJiIiIiIg4UUMmIiIiIiLiRA2ZiIiIiIiIEzVkIiIiIiIiTtSQiYiIiIiIOFFDJiIiIiIi4kQNmYiIiIiIiBM1ZCIiIiIiIk7UkImIiIiIiDhRQyYiIiIiIuJEDZmIiIiIiIgTNWQiIiIiIiJO1JCJiIiIiIg4UUMmIiIiIiLiRA2ZiIiIiIiIEzVkIiIiIiIiTtSQiYiIiIiIOFFDJiIiIiIi4kQNmYiIiIiIiBM1ZCIiIiIiIk7UkHUpMwtmtt4i/u5AM7uiwc89xMyua1dOkTxTnYm0n+pMRPKu6xoyM6uZ2Qwzm25mz5nZWWa2lEOOnczsqTE+x9fN7IRF/N0qZnaGmU02s2lmdr+ZHWdmS470vCGEc0MIe4wlWyPMbCMzu8XMXk4fV5rZRu0+rrSf6iw7dTaUmR2bnpDu1snjSnuozrJVZ2a2hJn9zMxeMLO6mf2zE8cVkfzruoYs9X8hhKWAtwJbA99s9gnMrK/lqZr3LuCyBT9oZssBNwCLA9uHEJYGdgeWAdbtaMLhPQPsBywHLA9cCvzWNZG0kuosQ8xsXZJ6m+ydRVpKdZYdp5G8n22Y/nqkbxwRyYtubcgACCE8DVwObAJgZh81s/vSK3CPmtkn53/u/CuAZvZVM3sWOCv9+D5mdruZTTWzf5nZZkO+pmZmXzKzO9OrZReY2WLpVb3LgVXTK5vTzWxVM9smHTF6Jb3aedKispvZssD6JG9UC/oiMA04KIRQS/+tT4YQPh9CuHPI5+1mZg+lo1M/NTNLn3uR0zbMbKKZXZpm/DdjeEMMIUwNIdRCCAEwYBBY6FQSyS/VmW+dDfET4KvA7BY8l2SM6sy3zsxsA+D/AZ8IIUwJIQyGEG4d7fOJSHfp6obMzNYguSp3W/qh54F9gAnAR4GTzeytQ75kZZKrXmsBn0j/7kzgk8BE4JfApWY2fsjXfADYC1gb2Aw4JITwKrA38EwIYan08QxwCnBKCGECyRvDhcPE3xO4KoQwuJC/2w34XQhh3gjfgn1Irqhunubcc4TPB/gpMBNYBfhY+lik9M37QyN8ztT0OX8MLHTKiuSX6sy/zszs/cDsEMIbRiCkGFRn7nW2LfA4cJwlUxbvMrN9G8ggItK1Ddkf0ibgOuAa0iYghBCHEB4JiWuAK4B3Dvm6ecCxIYRZIYQZwGHAL0MIN6VXwyYBs4DthnzNqSGEZ0IILwF/ArYYJtccYD0zWz6EMD2EcOMwn1tiIdM7UhNpbFpSNR2legL4xwjZMLNeYF/gmBDCqyGEu4FJw31NCGGzEMJ5I3zOMsAA8BleP5mQ/FOdJVzrzJJ7ik4AvtBAVskf1VnC+/1sdZLRyTqwKsn72SQz27CB7CLS5bq1IXtPCGGZEMJaIYRPp29GmNneZnajmb2UvsG9i+TepvmmhBBmDvnzWsBR6fSOqenXrEHyw3i+Z4f8/jVguBuuDyWZtnG/md1sZvss7JPMrIdkDv1fFvE8L5Jc8RtJM9kAVgD6gCeHfOzxBo4zovQq6y+AX5vZiq14TnGnOms+G7S+zo4DzgkhPDaG55DsUp01nw1aX2czSJrQ74QQZqdN8D+Aji7cIyL51K0N2Ruk0zIuAX4ArJSO2lxGcm/TfGGBL3sSOD59M5z/WCKEcH4Dh1zwuQghPBRCOABYEfgucLEtfBWprYFaCGHKIp77SuC96RtdK00B5pK8Sc+3ZgufvwdYAlithc8pGaI6a0ir62xX4HNm9mx6v9AawIVm9tUxPKdkmOqsIa2usztH/hQRkYVTQ/a6fmA86Q9pM9ubka9snQ58ysy2tcSSZlYys6UbON5zwEQzG5j/ATM7yMxWSOfKT00/vLA59cNN7wA4ieS+gUlmtlb63KuZ2UlDb9JuVjq//3dAxZLlfTcCPjLa5zOz3c3sLWbWa2YT0twvA/eN9jkl81RnI2h1nZE0ZJuQTOHagmR100+S3D8jxfT/27vzMDvKMv3j37c73QlrANkDWowDiCjOoAgqyiIKUgI/2RRUdFwQEYKISuGCR3ApWQQZZAQFQWCICq5TrLIKsgeCC3soNhOEBE8CBEKS+v1RFbOQdM7pszz1Vt2f6+orJGlO3Rzq6VNP1buozlagB3V2A/AYcIxzboxz7h3AjsAVHbymiNSEGrJClmWzgYnkE4+fBQ4kX4Z9pH/nDvJx96cX/85DwMdbPN59wEXA1GJ4yIbkk6X/6px7jnxC9IeWGlKy0DKXB17stWcCbycfPnGrc242cDX52PaHWsk3gsPIh4JMB86lWJ1reZxzf3XOfXg5f70G+XvQBB4mX2Fxt+X8N0sFqM5a1rU6y7JsRpZl0xd+kV8UP5tl2XMdZpSSUp21rJt19jKwF/l/T5O8wT2oeG9EREbksuwVIw2kxJxz6wF3Axtm+p8n0hOqM5HeU52JiOT0hMw/44Ev6MNLpKdUZyK9pzoTEUFPyERERERERMzoCZmIiIiIiIgRNWQiIiIiIiJG1JCJiIiIiIgYUUMmIiIiIiJiRA2ZiIiIiIiIETVkIiIiIiIiRtSQiYiIiIiIGFFDJiIiIiIiYkQNmYiIiIiIiBE1ZCIiIiIiIkbUkImIiIiIiBhRQyYiIiIiImJEDZmIiIiIiIgRNWQiIiIiIiJG1JB1iXPuHOfcP5xzf7HOIiIiIiIiflBD1j3nArtZhxAREREREX+oIeuSLMtuAGZa5xAREREREX+oIRMRERERETGihkxERERERMSIGjIREREREREjashERERERESMuCzLrDNUgnPuImBHYG3gKeAbWZad3clrBlEyBKy72Nc6i/3z2kAGPL+MrxeKX2cCD6ZxOL2THCJVF0TJmsBGwITFfh0PjAGGgEFgPvAyMA+YBTxZfD0BPJnG4Yz+JxfxRxAl41iyxiaQf54Nk9faGPLPtXnF1xxgOkWNFb9OS+Nwft/Di4j0kBqykgiiZAKwLbBd8esbgLW69PKzgAeA+4ApwGRgchqH/+zS64t4oWi83kpeY9sCm5JfFK7chZefA/wdeBC4DbgVuFWNmtRNcTPxTSyqs63Im7BXdeHl55Pf9HwMuJNFdfZAF15bRMSEGjIDQZSsBGzDouZrW/KLwn6bClwF/A64Oo3DlwwyiPRMECWvA3ZmUZ1tBrg+x3iI/KLxFuC6NA61ebxUSnGjYxfgbeR1tjUwrs8xZrLoRshNwPVpHM7tcwYRkVFRQ9YnQZSsAoTAvsDuwCq2iV7hOeBK4LdAorv64qsgSrYE9iOvtS2N4yzL/cDFwC/TOJxiHUZkNIIoWQv4AHmdvZt8aG+Z/JP8ZuPFwJW64SgiZaaGrIeCKBkEdgU+Bryf7gyL6of5wJ/Im7P/TeNwmnEekREFUbIVeRO2D7CFcZx2PAhcQt6cTbYOIzKSIErWJm/C9gN2Ip/z5YNZwO/Jm7PL0zh80TiPiMgS1JD1QBAlmwKfAj4KbGAcp1NzgYuAk9M4/LN1GJGFgigZC3wIOBx4s3Gcbvgz8N/ABWkczrEOI7JQECVvAyaS3/Ao25Owdj0LnA2cnsbho9ZhRERADVlXBVGyOXAs+UViFbcUuAo4KY3DK62DSH0Vd+kPBz5LvvJo1cwEzgR+kMbhU9ZhpJ6KER77AV8gn/NcNfPJhzSekMbhLdZhRKTe1JB1QRAlm7GoERs0jtMPfwa+Tz6cUZOmpS+CKNkYOAr4NP4M/+3Ei8BPgRPTOHzEOozUQ/Hk+ePAl4DX2qbpm+uBOI3Dy62DiEg9qSHrQA0bsaVNI//vPzuNQ51I0hNBlKwKfIX8Tv1Y4zgWXiYfynhcGodN6zBSXUGU7AOcBATGUaxcDxyhxXZEpN/UkI1CECUBcDxwAPVsxJZ2M/BZfYhJNwVR4oCPADGwoXGcMvgH8FXgnDQOF1iHkeoIouSNwKnkW0TU3XzgJ8DX0jh8xjqMiNSDGrI2FBeInwW+B6xqHKds5gOnA8emcTjLOoz4LYiSbYDTyPfqkyVNBiamcXiTdRDxW7F0/XHAIejm4tKeBRrAGWkczjPOIiIVp4asRUGUbEK+MtNO1llKbhpwVBqHF1kHEf8UG8yeTD6Hpd8bOPvmIuDzaRz+wzqI+CeIkoOB7wJrWWcpub+RjwC5wTqIiFSXGrIVKJ6KHUr+VKxsmzmX2TXkH2IPWAcRPwRR8k7gQmBj6ywemQ4clMbhVdZBxA/FKqXnAHtYZ/HIAvLmtaGnZSLSC2rIRlA8FTsH2NE4iq+eJ2/KzrcOIuVVLK99LPn8KA2bal9GvhDDV9M4fNk6jJRXECU7A+ejOZmjdTNwYBqHqXUQEakWNWTLEUTJp8gnOeupWOfOBg5L4/BF6yBSLkGUvJr8qdj21lkq4HbggDQOH7YOIuUSRMkY8rliR1PNPTL7qQkcnMbhL6yDiEh1qCFbShAlA+R3m4+0zlIxU4C90zicah1EyqFYYvsnwBrWWSpkNnBoGocXWAeRcihGelwEbGudpWLOAQ5P4/AF6yAi4j81ZIsJomQV8rv1e1lnqagZwH5pHF5rHURsBVFyLPBN6xwVdgIQaX/AeguiZDvg/4BXWWepqDuAUAvriEin1JAVgijZgPyDa2vrLBU3j3zjzTOsg0j/FfPFzgAOts5SAxcAn9C8snoKomQP4OfAStZZKm4qsGsahw9ZBxERf6khA4Io2Yq8GdPqbv1zahqHGhZaI0GUrARMAva0zlIjVwH7pHE42zqI9E+xpP0ZaJGcfnkaeH8ah7dZBxERP9V+cm8QJe8DbkTNWL99PoiSU61DSH8EUfIq8q0Q1Iz113uAG4IoWd86iPRHECXHAWeiZqyf1gGuDaIktA4iIn6q9ROyIEoOIp+Yqw8uO3pSVnFBlATAFcBmxlHqLAV2S+Pwfusg0hvFcOAzgU9aZ6mx+cBn0jg82zqIiPiltg1ZECX7A/+LmrEyOCWNwy9Yh5DuC6JkI/In0K+xziJMA96pZfGrJ4gSB5wLHGQcRfJ9AT+tpkxE2lHLIYtBlLyffMK7mrFyODKIkpOtQ0h3BVGyLvAH1IyVxQbA1UWTLNXyQ9SMlYUDzgqi5EPWQUTEH7VryIIo2Rm4GBiyziJL+EIQJSdZh5DuCKJkDfIFJTa3ziJLeA15U7aOdRDpjiBKYuCz1jlkCQPA+cXNXxGRFarVkMViNcU/AqtbZ5HlitM4PMY6hIxeECVjyeeM7WCdRZbrdmCnNA6ftw4ioxdEyRGAFkcqrxeAd6dxeIt1EBEpt9o0ZEGUbAzcDEywziIr9JE0Di+0DiHtC6JkgHxp+/2ss8gKXQrslcbhPOsg0r5iHvQk8iFyUl7PAO9I4/AB6yAiUl61GLJYDJ+6DDVjvjireJop/vkuasZ8sTtwmnUIaV8QJdsBP0PNmA/WBi4LomRN6yAiUl61aMjIV5/a0jqEtGxl4FdFIy2eCKJkT+DL1jmkLZ8NouQA6xDSuiBK1gJ+Doy1ziIt+zfy6xARkWWqfEMWRMmhwF7WOaRtrwUuLJZzlpIr9ho71ziGjM5ZQZRo8RUPFD8Pfwa82jqLtG3PIEqOsg4hIuVU6YYsiJI3AFpO3V+7A9+wDiEjC6JkmPyOvYbk+GlV4JdBlKxkHURW6MtAaB1CRi0OouTt1iFEpHwq25AVFxeTgHHWWaQjxwZRoguQcjsReKt1COnIG4HTrUPI8gVRsj3wLesc0pExwM+DKHmVdRARKZfKNmTAKWjeWBU44IIgSjaxDiKvFETJPsBE6xzSFZ8IokSbC5dQsW/cJPILevHbRuR7lGk4voj8SyUbsiBKPgB8xjqHdM0awI+sQ8iSgiiZAJxtnUO66n+CKHmtdQh5hbPRKsFV8j7g89YhRKQ8KteQFfuN6SKxet4bRMkHrUPIEk4FxluHkK5aGfihdQhZJIiSvYE9rHNI1x1XXK+IiFSvIQN+jBYXqKpTgihZ3TqEQBAl7wP2tc4hPbFrECXaS64EgihZFfiBdQ7pCf2/FZF/qVRDFkTJ7sCu1jmkZzZAk9rNFQvmaAGIajs1iJLVrEMI3ySfcyTV9IEgSt5vHUJE7FWmIQuiZAxwknUO6blDgyjZ2jpEzX2FfKNTqa4NgeOtQ9RZECVboQVz6uC/gyhZ2TqEiNiqTENGvojHFtYhpOcGgR8FUVKlc9cbxQbCX7bOIX1xWBAl/2kdoo6KFfh+hFZVrIMA+Lp1CBGxVYmL2iBKxgMN6xzSN9sAh1iHqKkzgGHrENIXC29+aHnu/vsk8DbrENI3RwVRohvKIjVWiYYM+BqwtnUI6avvaHPN/gqi5L3AztY5pK/eCuxtHaJOgigZSz53TOpjCA0RFqk17xuyIEr+DY2zr6PxwJHWIWrmWOsAYuJr1gFq5lPkc/ikXvYOomRL6xAiYsP7hgw4AQ2hqqvDgyhZwzpEHQRRsjPwDuscYuI/gijZ0zpEHQRRMgwcbZ1DTDh080OktrxuyIIo+Q9gH+scYmZ14PPWIWpCT8fqTYsO9Md/AdosuL72LxZOEpGa8bohAw63DiDmjtB+Sb0VRMm7gB2sc4iptxT7PEqPBFEyBBxjnUNMDaCnZCK15G1DVizocKB1DjG3BvmcC+kdPR0T0FOyXjsIeI11CDF3QBAl/24dQkT6y9uGjHxZ4HHWIaQUJgZRMmgdooqCKNkOeLd1DimF7YIo2cU6RBUV+yrq6ZhAvt2EzgWRmvGyISsuvg+1ziGlEaC5hL3yOesAUiqHWQeoqN2A11qHkNI4MIiSNa1DiEj/eNmQAXugoR2yJC2B32XFBcG+1jmkVMIgSrQke/cdbB1ASmUc+RBWEakJXxsy3aWVpW0XRMlm1iEq5iA0LFiWNAb4hHWIKika3NA6h5TOp60DiEj/eNeQBVHyejSnRZbtg9YBKuaT1gGklD4RRImzDlEhHyNvdEUWt2Uxh1dEasC7hgz4rHUAKS01ZF1S7PH3RuscUkqbANtbh6iQj1oHkNLSsEWRmvCqIStWotrPOoeU1pZBlGxpHaIidJEoI9H50QVBlLwF2MI6h5TWB4MoGbYOISK951VDBrwDWM86hJSanpJ1qLjxoT3+ZCT7BVEy1jpEBXzEOoCU2lqANmQXqQHfGrIPWAeQ0lND1rltgfWtQ0iprQG8yzpEBexlHUBKb0/rACLSe2rIpGo2K+Y/yejtah1AvLCbdQCfBVGyOfkeiiIj0c9jkRrwpiELouQN6MNLWrO/dQDP6UJbWqELxc6ozqQVGwZRogWWRCrOm4YMfXhJ67SZ8SgFUbIWsI11DvHClkGUbGQdwmNqaKVVuv4RqTifGjJ9eEmrNg2iZF3rEJ56D379XBBb+rk8CkGUjAN2sM4h3lCdiVScFxdeQZSsDLzTOod45W3WATylD35ph86X0XknsLJ1CPHG9sV1kIhUlBcNGfmHl5ZYlnZsZx3AU7rAlnbsEkTJoHUID2kImrRjLLCTdQgR6R1fGrKtrQOId/SErE1BlGwGbGidQ7yyJvAm6xAe2tE6gHhnR+sAItI7Y0b6S+fcbCBb3t9nWbZ61xMtmz7wpV1vCaJkMI3D+dZBVqREdabtAmQ0/gOYbB2iFWWotSBKxgBb9vo4Ujn6+SxSYSM2ZFmWrQbgnDsOmA6cDzjgw8BqPU+3iBoyadcqwFbAXdZBVqREdbZVH48l1eHNz+eS1NrmaAi+tM+bOhOR9rU6ZHHXLMvOyLJsdpZls7Is+x9gn14GW6hYjWrTfhxLKse3YYtmdVbQB76Mho/njWWt+fh+ib11gijZwDqEiPRGqw3ZfOfch51zg865Aefch4F+DQV7A6BJ4zIavi3sYVlnoAtFGR0fzxvLWvPx/ZJy0LkjUlGtNmQHAvsDTxVf+xV/1g/6ASSj9VbrAG0yq7MgStYENu7HsaRy1gii5NXWIdqkzzTxkc4dkYoacQ7ZQlmWpcBevY2yXPoBJKP1GusA7VCdicfeBDxmHaJVqjXxlM4dkYpq6QmZc24z59zVzrm/FL/fyjn3td5G+xf9AJLRGhdEydrWIVplXGda0EM64dXPaataC6JkHWD9Xh9HKsurOhOR1rU6ZPHHwDHAywBZlt0DfKhXoZbyxj4dR6ppI+sAbbCss6BPx5Fq8uppNHa1FvThGFJdvtWZiLSo1YZs5SzLblvqz+Z1O8zSgigZJt94VGS0fGrITOqsoLv20on1rAO0yarWVGfSiVWCKFnFOoSIdF+rDdkzzrnXUmyo6ZzbF5jWs1SLvKoPx5Bq86khs6oz8O+CWsrFt/PHqtZ8e5+kfHQOiVRQS4t6AJ8DzgJe55x7EniEfCPNXlurD8eQavOpIbOqM9CHvHTGtyc/VrWmOpNOrQ9MtQ4hIt3VakP2aJZluzjnVgEGsiyb3ctQi9ETMumUTw2ZVZ2BLhSlM76dP1a15tv7JOWjc0ikglodsviIc+4s8o12n+thnqWpIZNOTbAO0AaTOguiZAyqNenM2CBKxluHaIPVZ5pvTxKlfNSQiVRQqw3Z5sAfyId5POKcO905t33vYv2LhixKp3xqyKzqbF3A9eE4Um0+XSha1ZpP75GUk84hkQpqqSHLsmxOlmW/yLJsb+A/gdWB63uaLKe79tKpVa0DtKqbdeac2805d79z7iHnXLSCb19nNMcQWcq61gFa1a1aa7POQLUmnfOmzkSkda0+IcM5t4Nz7gxgMjAO2L9nqRbREzLp1JB1gHZ0o86cc4PAD4H3Aa8HDnDOvX6Ef2XcaLKKLGWsdYB2dFpro6gzUK1J57yqMxFpTUuLejjnHgHuBn4BfCnLsud7mmoRNWTSKW8asi7W2VuBh7Ism1q87iRgL+Bvy/n+Vhf3ERmJN+dRl2qt3ToDj94jKS2dQyIV1GphvynLslk9TbJsgwbHlGrxpiGje3U2AXh8sd8/AWw7wvfrA166wafzqBu11m6dgV/vkZSTziGRChqxsJ1zX86y7ATg2865bOm/z7JsYs+S5V7o8et7bX1mPnX80Dn3jmG+FmRYjgUMzIXQOsaIelBnyzofXvG6i2l56LLICEp/HnW51tqtM/DgPZLS0zkkUkErutNyb/HrHb0OshxzjI5beu8amHLPT4dOWG/QZTtaZym5fu7lNVrdrrMngI0X+/1GwN9H+P75XTqu1JsP51E3a63dOgM/3iMpN51DIhU0YkOWZdnvi3+8J8uyu/qQZ2lqyJbh62POv/4Tg5e93TmvhuNZKf2HVw/q7HZgU+fcJsCTwIeAA0f4/tK/R+KF0p9HXa61dusMPHiPpPR0DolUUKtjkb/vnNsA+CUwKcuyv/Yw0+LUkC1mHC/N+c3wsXe+buDxHayzeORl6wBt6EqdZVk2zzl3GHAF+TzMc1bwWvNGcxyRpfh0HnVca6OoM/DrPZJy0jkkUkEtNWRZlu3knFuffFngs5xzqwM/z7LsWz1Npzlk/xK4aY9fOvyVF1Z2L/Vj89Iq+ad1gFZ1s86yLLsUuLTFb9eND+mGF60DtKpbtdZmnYFqTTrnTZ2JSOtanhyaZdn0LMtOAw4hXy742J6lWkQfXsAeA3+645rho1Zd2b20uXUWD82wDtAOozr7Rx+OIdXn1XmkWhNP6RwSqaBW9yHbAvggsC/5Be4k4Kge5lqo5g1Zln1/6H9u+MDAje90TisrjdJM6wCtMqyzp4EFaPUu6cx06wCtMqy1p/pwDKk2b+pMRFrX6hyynwIXAe/NsmxFq0h1U20bslWYM/uy4eivrx54WvPFOuPTEzKTOkvjcH4QJTOAdfp1TKmcOWkc+rCi6UJWn2lqyKRTOodEKmiFDZlzbhB4OMuyH/Qhz9KeNTimuS3cow//ZvjrA2PdvO2ss1SAFw2ZcZ1B/iGvhkxGy5uLRONa8+Z9ktLSOSRSQSscopRl2XzgVc654T7kWVpqcExTHxm86pZLh49Zd6ybt4l1lorwoiEzrjPQMBjpjDcXica1pjqTTnlTayLSulaHLD4K3OSc+x3w/MI/zLLs+z1Jtchj5HtuDPb4OOYGWDD/J0Mn3bjz4N0aothdj1oHaINVnYE+5KUzvp0/VrXm2/sk5aNzSKSCWm3I/l58DQCr9S7OktI4nBdEyZPAq/t1TAtrMmvmlWOPfmQd11Qz1n0PWgdog0mdFfQhL53w7cmPVa2pzqQTs9M41HZAIhXU6j5k3+x1kBE8QoUbsre4+++dNHz8amPcgjdbZ6moB6wDtMq4zqYaHlv894h1gHYY1prqTDrhVZ2JSOtaXfb+WiBb+s+zLNu564le6X6gkk+ODhv89Y1HjfnlW5xjnHWWippJo+nTsveWdTalD8eQ6vLq/LGqtTQOZwZR8gSwUS+PI5XlVZ2JSOtaHbL4xcX+eRywDzCv+3GW6d4+HadvxjDv5Z8PH3/zmwcefJd1lorz5ulYwbLO7unTcaSafLtQtK41NWQyGr7VmYi0qNUhi3cu9Uc3Oeeu70GeZflbn47TFxswY/rlY49+erx7Qc1Y7/k0f8y0ztI4nBVESQoE/TieVMozaRz2cy+vjhl/pk0Bdu/TsaRa1JCJVFSrQxbXWuy3A8BbgPV7kuiVKtOQ7Thw9z1nD524/qDL3midpSbutg7QDuM6g/zDPujj8aQavLtINK41794vKQ2dOyIV1eqQxTtZNN5+Hvn+YJ/sRaClpXH4RBAlM4G1VvjNJXbsmJ9d/1+Dl7/dOYass9TIn+QLpwAAEl9JREFUzdYB2mRWZ4UpwF59PJ5Ug48XiZa15uP7JfampXH4tHUIEemNERsy59w2wONZlm1S/P5j5GPtU/r75Oo6YO8+Hq9rxvHSnN8Of33y5gNPVHJhkhKbC0y2DtGKEtWZLhRlNLw5b0pSaw8Cc4CV+nQ8qQZv6kxE2jewgr8/k/zCFufcu4DvAucBTeCs3kZbwjV9PFbXBG7a45PHHvLY5gNPvMM6Sw3dRaP5knWIFpWlzrwa4iml4dN5Y15raRzOB/7cj2NJpfhUZyLSphU1ZINZli1cNvyDwFlZll2SZdnXgX/vbbQlXN3HY3XFHgN/uuOa4aNWXdm9tLl1lpryabhiKeosjcOpwKP9Op5UwtP41VyUotbw9CajmNI5I1JhK2zInHMLhzW+myV/ILQ6/6xjaRzeB3iyileWnTL0w+tOGzp96wHHmtZpasyrhqwMdVa4os/HE79dlcbhK/bzKrGy1JrqTNoxB/ijdQgR6Z0VfQBdBFzvnHuGxX4gOOf+nXyIRz9dA3ykz8dsy6q8MOuy4ejejQee2dE6S80tAK61DtGGMtXZ5cDBfT6m+Mu3xqIstXYTMBtYrY/HFH9dn8bhi9YhRKR3RnxClmXZt4GjgHOB7bMsW3gndAA4vLfRXqHUj+u3cI8+fOfYQ2ZsPPDMttZZhFtpNL1ZjapkdXY1/dsgV/yW4VlDVpZaS+PwZfy6aSS2LrcOICK9tcIhGlmW3bKMP3ugN3FGVNp5ZB8dvPKW48ac+wbnWNU6iwDwf9YB2lWWOis2iL4ZeGe/jy3emZLG4VPWIdpVllojv8je0+C44h+vbnyISPtWNIesNNI4fAx42DrH4gZYMP+nQ9+7/vihc7dTM1Yqv7cO4Dl9+EsrdNe+M3r/pBWPFvPoRaTCvGnICpdZB1hoLZozbht76JSdBqdof7FyeZRG06dV38pIF4rSCjXuHUjj8BHyPclERqI6E6kB3xqy86wDAGzj7rv3trGfm7O2m7W1dRZ5Be+GK5bQZOAx6xBSas+QL0whnfmNdQApPZ0jIjXgVUOWxuEdGO95M3HwVzf+Yvi4Tca4BRtZ5pDlutA6gO+KZcz1PspIJhULU0hnzrcOIKX2FHCldQgR6T2vGrLCTy0OOsS8ub8aPvaPXxi6eHvnGGeRQVboPhpNn/YfKzNdKMpIdH50QRqHfwamWOeQ0roojcP51iFEpPd8bMguAPp6Z3YDZky/c+wh92898JBWnis3k2a9itI4vBe4wzqHlNL9aRzeZh2iQn5mHUBKSzc+RGrCu4YsjcOn6eM8oZ0G7ppy49iJg6u7F97Yr2PKqMxDFzbddrZ1ACklnRfddT4w1zqElM7kNA4nW4cQkf7wriErnNOPgzTGnHvDOUMnbjnosnX6cTzpyOU0mtOtQ1TMhcDz1iGkVOaSb6osXVLcZNTCDbK0H1sHEJH+8bUhuwyY1qsXX4mX5lw5/KWbPj7mync5t+LNs6UUdNe+y9I4nA1Mss4hpfLrooGQ7jrLOoCUynNoYSWRWvGyISsmufZkbHXgpj1+59jPPLbZwJPv6MXrS0/cD/zOOkRFnW4dQEpF50NvXAP8zTqElMbPihtiIlITXjZkhdOBl7r5gnsN3HTHNcNHrbaym7t5N19Xeu5EGs0F1iGqKI3Du9HebpK7Lo3DG61DVFGx1cS3rXNIKcwFYusQItJf3jZkaRw+TteGeWTZD4ZOv+7UoR9uPeBYozuvKX3yJFqJqteOsw4gpaDzoLcmkT/tl3o7r7i+EZEa8bYhK3wHeKGTF1iVF2bdNHbi7XsN/mlH57x/P+roFBpNrVDWQ2kc3g5cbp1DTN2YxuG11iGqLI3DBeSfaVJf84DvWocQkf7zugFJ43A68MPR/vuvd+nDk8ceMmOCm/HWLsaS/nkWONM6RE180zqAmDreOkBNXAg8bB1CzFyQxuEj1iFEpP+8bsgK3wPanvx60OAVNyfDX1lv2M3bpAeZpD9OpNF8zjpEHaRxeAvwB+scYuKWNA6vtA5RB8WCVXpKVk/z0TxCkdryviFL43AGcGqr3z/AgvnnDcXXHTd03tucY9UeRpPeehQ4xTpEzegpWT3p6Vh//QxIrUNI312UxuFD1iFExIb3DVnhZPLhayNai+aM28YeOmWHwXt27H0k6bFjaDRftA5RJ8UKe7+3ziF9dV0ah5dah6iTNA7nAV+xziF9NQf4hnUIEbFTiYYsjcMmcNJI37ONu+/e28Z+7sW13ayt+xRLeucWGs2LrEPU1EQ6XEhHvDEX+Kx1iDpK4/Ai4GrrHNI3307jcKp1CBGxU4mGrHAqsMzJsEcMXnLjL4aP22SMWzChz5mkN460DlBXaRymaAhbXZyUxuF91iFq7FC6vNemlNJ9wInWIUTEVmUasjQOXwA+s/ifDTFv7q+Hj/3jkUOXbO8c44yiSXddSKN5i3WImjsZ+Jt1COmpR4BvWYeoszQOHwBOsM4hPXdoGofaukWk5irTkAGkcXgV+YRoNuSZaXeO/cwD/znw0DuNY0n3PA183jpE3aVx+DIaylZ1n0vjcI51COE7aBn8KrtA+/uJCFSsISscuevA7df/cewRY1Z3c95gHUa6aiKN5jPWIQTSOLwBOM86h/TEJWkcXmYdQiCNwxeBz1nnkJ54FjjKOoSIlEPlGrI0DmeeOXzKaYMuW8c6i3TVJTSak6xDyBK+SP7UUqrjn+gpdKmkcXgFoEWMqufoNA7/YR1CRMqhcg0ZAI3mr4DzrWNI1zwFHGIdQpaUxuEzwEeABdZZpGv+K43DJ6xDyCscgoYuVskv0jj8sXUIESmPajZkucNZzqqL4pUM+ISGKpZTGodXks9zEf+dmsbhb6xDyCulcTgL2A+tulgFDwKfsg4hIuVS3Yas0WwCe5NvuCj++haNpjamLbdvAJqY7rdbgS9bh5DlS+PwLjSc1HcvAvulcTjbOoiIlEt1GzKARvNuNNTNZ5cBDesQMrI0DhcAB5IPLRX/zAT2L1bPlBJL4/BHgObS+mtiGodTrEOISPlUuyEDaDR/BvzQOoa0bSrwYRpNzU/yQBqH04ED0Hwy32TAQWkcPmYdRFr2aeB+6xDStgs0b0xElqf6DVnuSOBP1iGkZXOAvWk0n7UOIq0r9tP5unUOaUucxmFiHUJal8bhc+TzyZ6zziIt+wsarSMiI6hHQ9ZovgzsQ/7URcptAfBRGk0N6/BQGoffAX5inUNachHwVesQ0r40Dv8M7AvMs84iK/Qk8L40Dp+3DiIi5VWPhgyg0ZwOvBeYbh1FRnQIjeYl1iGkI4cAeupSbtcCH0/jMLMOIqNT7E+m1frKrUnejGkrCREZUX0aMoBG82FgV/LNT6V8vkKjqTH2nkvjcD6wPxomXFZ3AR9I43CudRDpTBqH5wGRdQ5ZpjnAXsXTTBGREdWrIQNoNO8B9kDL4ZfNyTSa37UOId2RxuELwO7kF/9SHn8D3pvGYdM6iHRHGoffQ3sBls1cYJ80Dq+3DiIifqhfQwbQaN5IPv5em2yWw9nAl6xDSHcVF/3vJW8CxN5U4D1pHGqT9YpJ4/CrwA+scwgA84ED0zi8zDqIiPijng0ZUGw2vDtaqcraKcCnaTQ1l6WCiov/nYE7rbPU3F+BHdI4/Lt1EOmZI4ETrEPU3Ivke/ppHrSItMVlWc2vgxvjtwEuBda2jlJDX6PR/LZ1COm9IEpWBS4mn8Mp/XUD+VwWzZ2tgSBKJpLf6KrvDVcbzwJ7pnF4o3UQEfGPGjKAxvgtgCuBjayj1MQC4HM0mj+yDiL9E0TJEPmS+AdZZ6mRXwIfTeNQw7NrJIiS/YDzgbHWWWriMWC3NA7vtQ4iIn5SQ7ZQY/yrgcuA11tHqbgXgY/TaP7cOojYCKLkO8Ax1jlq4DTgyDQOF1gHkf4LouRdwG+BNayzVNw95EvbaziwiIyaGrLFNcavCvyUfMEP6b4U2IdGc7J1ELEVRMmhwH+jYVW9kAFHp3F4onUQsRVEyZbA5Wj0R69cC/y/NA5nWQcREb+pIVuWxvgvky8jPGgdpUKuAg6g0ZxhHUTKIYiSncmHVW1onaVCnibf8PlS6yBSDkGUrAucB+xmnaVCMuBE4GtpHL5sHUZE/KeGbHka498NTEKLfXRDDHyVRlNDp2QJQZSsTf5U+v3WWSrgD+TzxaZbB5FyCaLEAV8gv9E4bBzHd9OBg9I4vMo6iIhUhxqykTTGb0y+R9Z7rKN4ahpwMI3m/1kHkXILouRw8jvOWoSgfS8DXwdOSONQP9BluYIoeTNwEbCpdRZPXQZ8LI3Dp62DiEi1qCFrRWP8wcBJwGrWUTxyPnAEjeaz1kHED0GUvIn8YnEL6ywemQockMbhbdZBxA/FFhSnAx+zzuKRuUAEnKqbHiLSC2rIWpWvwvgT9LRsRaYBn6HR/L11EPFPECUrA98EjgCGjOOU2TzgR8BX0jicbR1G/BNEyb7A94GNrbOU3M3AoWkc3m0dRESqSw1ZuxrjP0k+Dn9d6yglMx84BzhaT8WkU0GUbEa+ue3u1llK6GrgiDQO/2odRPxW3AA5GvgSsJJxnLJ5kny10gutg4hI9akhG43G+NXIP8S+gD7EIB9X/yUaTV0gSlcFUbI7+V38za2zlMBU4Kg0Dn9jHUSqJYiS15DP4dzPOksJvAicDHw3jcPnrcOISD2oIetEY/wE4NvAR6nnfkpTgC/SaP7BOohUVxAlQ8BE8oUrxhvHsfA8+VP5k9M4fMk6jFRXECU7AD8A3mSdxcivgC+mcfiIdRARqRc1ZN3QGL8V+ROz/YExxmn64S7yRU4maSl76ZcgStYEDiNvzuqwHcWz5IsvnJbG4TPWYaQegigZAD4AHAO82ThOPywAfg3EaRzeYR1GROpJDVk35cvkHwF8GljdOE23ZcClwMk0mtdah5H6Kua9fIq8MXutcZxeeJS8ETtTC3aIpSBKdgG+CLwXcMZxum0O8L/ASWkc3mcdRkTqTQ1ZLzTGr05+wXgI/u/3Mpt8g+xTaDTvtQ4jslBxJ3938sZsF/y/YLweOA34bRqH863DiCwURMnmwOHkS+WvahynU48DZwA/TuNwhnUYERFQQ9Z7jfFvAw4C9sWfYVbzgD+Q7yX2axrNOcZ5REYURMnGwD7kdfZ2/GjOMuA24GLg4jQOU9s4IiMLomQl4H3ki3+E+LM35xPk88MuBm5K41BD7UWkVNSQ9Utj/BhgZ/KLxl2Af7MN9AqzyO/Q/468CdOdQ/FSECUbAnuTXzRuT7kW3MmAP5FfGF6SxuHjxnlERiWIknHAruQ3QfagfAvuPAZcAvwSuEUbOotImakhs9IYH5A3aO8ufl2/zwleBG4CriHf1+gOGk0Nk5JKCaJkPWAHYNvi683AuD5GeIl8EZxbgVuAG9I4/Hsfjy/Sc0GUDJPf/NiORbW2Xp9jPEheZ7eSPwW7q8/HFxEZNTVkZdEY/xrgDUt9vY7OLx4z8kUC/rLU1700mnM7fG0RrxRL6G/FoovGTYGNgA3obIXU+cA08s1kH2LRheHdaRyqzqR2gigJWFRnW5HX2UbAKh2+9EzyIYiPAXeS19ltmg8mIj5TQ1ZmjfEOeBWwbvG1TvHrmix7GNbzwD+Kr6f/9c9darycc7uR71EzCPwky7K4G68rYq1YIGQ9YAL5ReME8iFYY4Ah8nN+PvAy+RzLWeTN15PkF4dPaSEOkRULomQ8i2psAvln2jCLam0BeY3NIx/JMY28xp4EnkzjUHOaRaRy1JBJS5xzg8ADwHvIPxxvBw7IsuxvpsFERERERDxWpsnuUm5vBR7KsmxqlmVzyZfC38s4k4iIiIiI19SQSasmkO/fstATxZ+JiIiIiMgoqSGTVi1rXyeNdxURERER6YAaMmnVE8DGi/1+I0DLd4uIiIiIdEANmbTqdmBT59wmzrlh4EPkm0iLiIiIiMgodbLvjtRIlmXznHOHAVeQLwF+TpZlfzWOJSIiIiLiNS17LyIiIiIiYkRDFkVERERERIyoIRMRERERETGihkxERERERMSIGjIREREREREjashERERERESMqCETERERERExooZMRERERETEiBoyERERERERI2rIREREREREjKghExERERERMaKGTERERERExIgaMhERERERESNqyERERERERIyoIRMRERERETGihkxERERERMSIGjIREREREREjashERERERESMqCETERERERExooZMRERERETEiBoyERERERERI2rIREREREREjKghExERERERMfL/AfWEgH8kE+KiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x576 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,8))\n",
    "i = 0\n",
    "for parch in df['Parch'].unique():\n",
    "    fig.add_subplot(2, 4, i+1)\n",
    "    plt.title('Parents / Child : {}'.format(parch))\n",
    "    df.Survived[df['Parch'] == parch].value_counts().plot(kind='pie')\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AnandNoctis\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "replacement = {\n",
    "    6: 0,\n",
    "    4: 0,\n",
    "    5: 1,\n",
    "    0: 2,\n",
    "    2: 3,\n",
    "    1: 4,\n",
    "    3: 5\n",
    "}\n",
    "df['Parch'] = df['Parch'].apply(lambda x: replacement.get(x))\n",
    "df['Parch'] = StandardScaler().fit_transform(df['Parch'].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('Ticket', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.827377</td>\n",
       "      <td>-0.797294</td>\n",
       "      <td>male</td>\n",
       "      <td>-0.557420</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.468807</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.566107</td>\n",
       "      <td>1.537975</td>\n",
       "      <td>female</td>\n",
       "      <td>0.649410</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.468807</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.827377</td>\n",
       "      <td>1.070922</td>\n",
       "      <td>female</td>\n",
       "      <td>-0.255712</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.468807</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.566107</td>\n",
       "      <td>1.537975</td>\n",
       "      <td>female</td>\n",
       "      <td>0.423129</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.468807</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.827377</td>\n",
       "      <td>-0.797294</td>\n",
       "      <td>male</td>\n",
       "      <td>0.423129</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.468807</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived    Pclass      Name     Sex       Age  SibSp  \\\n",
       "0            1         0  0.827377 -0.797294    male -0.557420      1   \n",
       "1            2         1 -1.566107  1.537975  female  0.649410      1   \n",
       "2            3         1  0.827377  1.070922  female -0.255712      0   \n",
       "3            4         1 -1.566107  1.537975  female  0.423129      1   \n",
       "4            5         0  0.827377 -0.797294    male  0.423129      0   \n",
       "\n",
       "      Parch     Fare Cabin Embarked  \n",
       "0 -0.468807   7.2500   NaN        S  \n",
       "1 -0.468807  71.2833   C85        C  \n",
       "2 -0.468807   7.9250   NaN        S  \n",
       "3 -0.468807  53.1000  C123        S  \n",
       "4 -0.468807   8.0500   NaN        S  "
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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